Spectrum Usage Anomaly Detection from Sub-Sampled Data Stream via Deep Neural Network

被引：4

作者：

Zhang H. ^{[1
]}

Yang J. ^{[2
]}

Chen J.T. ^{[3
]}

Gao Y. ^{[1
,4
]}

机构：

[1] Institute for Communication Systems, The University of Surrey, Guildford

[2] Mobile Communications Innovation Center, China Academy of Information and Communications Technology, Beijing

[3] Future Network Intelligence Institute (FNii) and School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen

[4] School of Computer Science, Fudan University, Shanghai

来源：

Journal of Communications and Information Networks | 2023年 / 8卷 / 01期

基金：

英国工程与自然科学研究理事会; 中国国家自然科学基金;

关键词：

anomaly detection; compressive sensing (CS); machine learning (ML); multicoset sampling;

D O I：

10.23919/JCIN.2023.10087244

中图分类号：

学科分类号：

摘要：

Anomaly detection is an essential part of any practical system in order to remedy any malfunction and accident early to create a secure and robust system. Malicious users and malfunctioning cognitive radio (CR) devices may cause severe interference to legitimate users. However, there are no effective methods to detect sponta-neous and irregular anomaly behaviors in sub-sampling data stream from wideband compressive spectrum sensing as an important function of a CR device. In this article, to detect anomaly utilization of spectrum from sub-sampled data stream, a multiple layer perceptron/feed-forward neural network (FFNN) based solution is proposed. The proposed solution would learn the pattern of legitimate and anomalous usages autonomously without expert’s knowledge. The proposed neural network (NN) framework has also shown benefits such as more than 80% faster detection speed and lower detection error rate. © 2023, Posts and Telecom Press Co Ltd. All rights reserved.

引用

页码：13 / 23

页数：10

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